Title
Motion and Appearance Nonparametric Joint Entropy for Video Segmentation
Abstract
This paper deals with video segmentation based on motion and spatial information. Classically, the motion term is based on a motion compensation error (MCE) between two consecutive frames. Defining a motion-based energy as the integral of a function of the MCE over the object domain implicitly results in making an assumption on the MCE distribution: Gaussian for the square function and, more generally, parametric distributions for functions used in robust estimation. However, these assumptions are not necessarily appropriate. Instead, we propose to define the energy as a function of (an estimation of) the MCE distribution. This function was chosen to be a continuous version of the Ahmad-Lin entropy approximation, the purpose being to be more robust to outliers inherently present in the MCE. Since a motion-only constraint can fail with homogeneous objects, the motion-based energy is enriched with spatial information using a joint entropy formulation. The resulting energy is minimized iteratively using active contours. This approach provides a general framework which consists in defining a statistical energy as a function of a multivariate distribution, independently of the features associated with the object of interest. The link between the energy and the features observed or computed on the video sequence is then made through a nonparametric, kernel-based distribution estimation. It allows for example to keep the same energy definition while using different features or different assumptions on the features.
Year
DOI
Venue
2008
https://doi.org/10.1007/s11263-007-0124-2
International Journal of Computer Vision
Keywords
Field
DocType
Spatio-temporal segmentation,Nonparametric distribution,Joint entropy,Active contour
Computer vision,Joint probability distribution,Computer science,Motion compensation,Image segmentation,Multivariate normal distribution,Parametric statistics,Joint entropy,Artificial intelligence,Motion estimation,Kernel method
Journal
Volume
Issue
ISSN
80
2
0920-5691
Citations 
PageRank 
References 
2
0.37
29
Authors
5
Name
Order
Citations
PageRank
Sylvain Boltz1465.61
Ariane Herbulot2497.28
Eric Debreuve326021.54
Michel Barlaud42317310.53
Gilles Aubert51275108.17